New EdTech tools with AI features appear weekly. Each promises transformation. How do you evaluate which ones are worth adopting—and which create more risk than value?
This guide provides a practical evaluation framework for schools.
Executive Summary
- EdTech AI tools require evaluation on both pedagogical and data protection dimensions
- Most tools aren't worth the implementation effort—be selective
- Key questions: Does it serve learning? Does it protect student data? Does it integrate with existing systems?
- Pilot before committing; evaluate pilot rigorously
- Vendor stability and support matter as much as features
- Build evaluation into your procurement process, not as an afterthought
Evaluation Framework
Dimension 1: Educational Value
Does this tool serve genuine learning needs?
| Question | Red Flag | Green Flag |
|---|---|---|
| What learning problem does this solve? | "It's cool" / "Other schools use it" | Specific, measurable learning gap |
| Is there evidence of effectiveness? | No research, only vendor claims | Independent studies, peer school references |
| Does it align with our curriculum? | Requires curriculum change to fit tool | Enhances existing approach |
| What's the teacher's role? | Replaces teacher judgment | Augments teacher capability |
| Does it improve over existing solutions? | Marginal improvement, high switching cost | Clear advantage over status quo |
Decision tree: Educational Value
Dimension 2: Data Protection
Does this tool adequately protect student data?
| Question | Red Flag | Green Flag |
|---|---|---|
| What student data is collected? | Vague or excessive scope | Clear, minimal data |
| Where is data stored? | Unclear jurisdiction | Local or well-regulated jurisdiction |
| Who has access to data? | Undefined, broad access | Specific, limited access |
| How is data protected? | No certifications | SOC2, ISO27001 |
| Is data used for AI training? | Yes, or unclear | Explicit no, contractually prohibited |
| What happens when we stop using? | Data retained indefinitely | Clear deletion process |
Apply vendor evaluation framework from your data protection practices.
Dimension 3: Integration and Operations
Will this tool work in our environment?
| Question | Red Flag | Green Flag |
|---|---|---|
| Does it integrate with our SIS/LMS? | Manual data entry required | Native integration or API |
| What training is required? | Extensive training, ongoing dependency | Intuitive, minimal training |
| What support is available? | Email only, long response times | Responsive support, local presence |
| What does implementation involve? | Months of setup | Quick deployment with support |
| What's the total cost of ownership? | Hidden costs, per-student fees that scale | Transparent, predictable pricing |
Dimension 4: Vendor Viability
Will this vendor be around and responsive?
| Question | Red Flag | Green Flag |
|---|---|---|
| How long has the company existed? | Brand new, pre-revenue | Established, sustainable |
| What's their funding situation? | Burning cash, uncertain runway | Profitable or well-funded |
| Do they serve schools like ours? | First school customer | Many similar references |
| What's their product roadmap? | Unclear or irrelevant | Aligned with school needs |
| What if they're acquired? | No plan | Data protection survives |
Evaluation Process
Stage 1: Initial Screening (1-2 hours)
Before spending significant time:
- Does it address a real need we've identified?
- Is pricing within our range?
- Do they serve schools our size/type?
- Any obvious data protection concerns?
Decision: Advance to detailed evaluation or stop.
Stage 2: Detailed Evaluation (1-2 weeks)
For tools that pass screening:
- Request demo focused on your use cases
- Review privacy policy and terms
- Check references from similar schools
- Assess integration requirements
- Evaluate against framework dimensions
Decision: Advance to pilot or stop.
Stage 3: Pilot (4-8 weeks)
For promising tools:
- Define pilot scope (which classes, teachers, duration)
- Establish success metrics
- Collect feedback systematically
- Evaluate against defined metrics
- Assess actual vs. promised data practices
Decision: Adopt, extend pilot, or reject.
Stage 4: Adoption Decision
For successful pilots:
- Negotiate contract terms (especially data protection)
- Plan implementation and training
- Communicate to stakeholders
- Establish ongoing monitoring
Quick Evaluation Checklist
Educational Value
- Addresses specific, documented learning need
- Evidence of effectiveness (not just vendor claims)
- Aligns with our pedagogical approach
- Enhances rather than replaces teacher judgment
- Teachers support adoption
Data Protection
- Data collection scope is appropriate and minimal
- Data storage location meets requirements
- Security certifications are current
- No use of student data for AI training
- Clear deletion process
- Willing to sign DPA
Operations
- Integrates with existing systems
- Training requirements are manageable
- Support is responsive and accessible
- Implementation timeline is realistic
- Total cost is acceptable and predictable
Vendor
- Company is established and sustainable
- References from similar schools
- Roadmap aligns with our needs
- Acquisition contingency acceptable
Overall
- Benefits clearly outweigh costs and risks
- Staff capacity exists for implementation
- Fits within broader technology strategy
Frequently Asked Questions
Q1: How do we handle tools teachers find and want to use?
Create a simple request process. Teachers submit tool for evaluation; IT/admin screen quickly. Approve, pilot, or reject with explanation.
Q2: What about free tools?
"Free" often means your data is the product. Evaluate free tools the same as paid—data protection matters regardless of cost.
Q3: Should we wait for tools to mature?
Balance early adoption benefits against risk. For critical functions, prefer mature tools. For low-stakes experimentation, early adoption may be acceptable.
Q4: How many EdTech tools should we have?
Fewer is generally better. Tool proliferation creates complexity, training burden, and data sprawl. Prioritize depth over breadth.
Q5: What if a tool doesn't pass evaluation but teachers love it?
Understand why teachers love it. If the benefit is genuine, work with vendor on data protection improvements. If risks are too high, help teachers find alternatives.
Next Steps
Apply this framework to your next EdTech evaluation. Build it into your procurement process so evaluation happens consistently.
Need help evaluating EdTech AI tools?
→ Book an AI Readiness Audit with Pertama Partners. We'll help you assess tools against both pedagogical and data protection requirements.
References
- Common Sense Education. (2024). EdTech Privacy Evaluation Framework.
- ISTE. (2024). Selecting Digital Tools for Learning.
- LearnPlatform. (2024). EdTech Evidence Standards.
Frequently Asked Questions
Assess educational effectiveness, data privacy practices, integration with existing systems, total cost, vendor stability, accessibility, and whether it's designed for education contexts.
Ask about student data collection, storage location, sharing practices, retention periods, security measures, breach notification procedures, and whether data is used for AI training.
Include licensing, implementation, training, integration, ongoing support, and staff time. Consider per-student costs and total cost of ownership over 3-5 years.
References
- Common Sense Education. (2024). EdTech Privacy Evaluation Framework.. Common Sense Education EdTech Privacy Evaluation Framework (2024)
- ISTE. (2024). Selecting Digital Tools for Learning.. ISTE Selecting Digital Tools for Learning (2024)
- LearnPlatform. (2024). EdTech Evidence Standards.. LearnPlatform EdTech Evidence Standards (2024)
